Robust Development of Active Learning-Based Surrogates for Induction Motor Article Swipe

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Janne Keränen , Mikko Tahkola , Peter Råback , Álvaro González , Victor Mukherjee , Jenni Pippuri-Mäkeläinen ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1109/tmag.2023.3324796

A robust open-source cloud-based workflow is developed for finite element (FE) data generation for active learning (AL) -based surrogate modelling. Special attention is paid to making the FE solution procedure as robust and fast as possible without human intervention by, e.g., implementing special convergence criteria, reliable parallel computation, and variable timestep length. In AL, a surrogate model automatically improves itself by iteratively querying more FE data. Using AL and large datasets generated with parallelised cloud FE simulations, we develop a surrogate model to rapidly predict induction machine steady-state torque, torque ripple, total losses, and current harmonic distortion, as a function of motor frequency, voltage, and slip. Results show that AL performs better than grid sampling and on average works as well as random sampling, but with some outputs, the results vary less with AL. In addition, accurate ripple estimation requires a much larger training dataset than the other variables.

Related Topics

Computer Science

Induction Motor

Torque

Finite Element Method

Algorithm

Voltage

Artificial Intelligence

Physics

Quantum Mechanics

Concepts

Computer science Torque ripple Induction motor Torque Ripple Finite element method Algorithm Control theory (sociology) Voltage Artificial intelligence Direct torque control Thermodynamics Physics Control (management) Quantum mechanics

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/tmag.2023.3324796
PDF: https://ieeexplore.ieee.org/ielx7/20/4479871/10286312.pdf
OA Status: hybrid
Cited By: 3
References: 11
Related Works: 10
OpenAlex ID: https://openalex.org/W4387681947

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DOI: https://doi.org/10.1109/tmag.2023.3324796

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Title: Robust Development of Active Learning-Based Surrogates for Induction Motor

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-10-16

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Authors: Janne Keränen, Mikko Tahkola, Peter Råback, Álvaro González, Victor Mukherjee, Jenni Pippuri-Mäkeläinen

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Landing page: https://doi.org/10.1109/tmag.2023.3324796

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Concepts: Computer science, Torque ripple, Induction motor, Torque, Ripple, Finite element method, Algorithm, Control theory (sociology), Voltage, Artificial intelligence, Direct torque control, Thermodynamics, Physics, Control (management), Quantum mechanics

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Cited by: 3

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Citations by year (recent): 2025: 2, 2024: 1

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